Transportation mobility and safety problems are of extreme importance internationally. Accordingly, there is an urgent need to develop a means of correcting and improving urban transportation mobility and safety modeling that connects these two independent research domains. The failure to meet this need is a pressing national and international problem because in the absence of advancements in modeling, safety risk and mobility disruption will continue to result in wasted time and unnecessary loss of life and remain a burden on the economy. In light of these, the key research objective is a synergistic US-Korea collaboration to create a unified data-driven algorithmic framework for autonomously predicting mobility and safety impacts of highway rehabilitation by harnessing artificial intelligence (AI). To meet this timely need, the research team proposes a new initiative: three annual Advanced Transportation Infrastructure Informatics Institutes (ATI3) to train U.S. students in cutting edge skills in collaboration with counterparts in Korea. The institutes will catalyze an international collaboration where the best infrastructure mobility and safety analysis practices are synergistically integrated into a unified AI data-driven algorithmic framework that has intellectual merit with regards to the NSF Big Ideas of ?Harnessing the Data Revolution? and ?Convergence Research?. This IRES will deploy 15 graduate students from participating U.S. universities; each student will spend three weeks annually over the three-year duration at KAIST in Daejeon, Korea. Korea?s unique transportation challenges and KAIST?s solutions will offer U.S. students truly exceptional, enriching international high-impact learning and research experiences in this important research area. The proposed approach is unique because it models the level of mobility and safety disruption by accounting for big data analytics and AI techniques (ATI3 I), drivers? behavior modeling fused from hybrid simulations (ATI3 II), and high-fidelity prediction that accounts for drivers? behavior as a major influence with a reinforcement AI deep-learning algorithm (ATI3 III).

The proposed research activities at ATI3 will be tightly interwoven with a comprehensive education plan, with the overall goal of promoting students? transformational learning through a project-based research-centric environment while widely engaging practitioners and communities in the project-based research projects. The central hypothesis is that instituting a three-year annual ATI program for learning new methods and techniques, then leveraging the techniques to model the level of safety and mobility disruption due to highway rehabilitation, will allow the IRES fellows to research new discoveries that may correct and improve the results of work zone safety and mobility modeling. The new safety-mobility integration system will provide a rigorous theoretical basis for comparatively analyzing rehabilitation alternatives so that motorist inconvenience and safety risk can be assessed in a fundamentally new way. It will provide insights into new interactions between drivers? stochastic route choice behaviors and their consequences in traffic queue delays and crash risks. Once successfully completed, the IRES will result in the research community and practitioners with the first view of a systematic estimation method to determine the safest and most economical transportation plans that would be smarter (better mobility, less travel time, and lower road user cost) and greener (reduced vehicle operating costs and environmental costs) than those in existence today.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

National Science Foundation (NSF)
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Fahmida Chowdhury
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Texas A&M University
College Station
United States
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